Insightly

Align Marketing, Sales, and Project teams around one view of your customers.

4.3 (4 Reviews)
About Insightly
Insightly provides customer relationship management software for small and midsize businesses across a range of industries such as manufacturing, consulting, health & wellness, media and others. With more than 1.5 million users worldwide, Insightly is the world’s most popu...
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Insightly
Align Marketing, Sales, and Project teams around one view of your customers.
4.3 (4 Reviews)
Product Demo
Core Features
CRM Software Features
  • Calendar & Task
  • Custom Dashboard
  • Lead Management
  • Reporting
  • Sales Automation
  • Contact Management
  • Collaboration Tools
  • Email Integration
  • File Management
  • Forecasting & Analytics
  • Mobile Access
  • Pipeline Management
  • Security
  • Workflow Automation
2 Reviews
Client Reviews
Jose GarciaReviewed on 6/3/19
Well crafted and always improving
Role: Group CEO at Maxprom.com
Reviewed on 6/3/19 by Jose Garcia
Role: Group CEO at Maxprom.com
Well crafted and always improving
We have used Insightly for over a year and IT IS GREAT. Super friendly and the APP is always up and ready to serve almost with all the functionality of the pc version. Great software.
ComboAppReviewed on 4/12/19
Insightly for the win
Reviewed on 4/12/19 by ComboApp
Insightly for the win
Discussions
  • Big Data
1 Answer
 Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details.  Here is a review of the four steps of the data development process:Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:internet clickstream data;webserver logs;mobile applications;cloud applications; social media content;text from customer and survey responses;mobile phone records; andmachine data obtained by sensors connected to the internet of things (IoT).Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:data mining, which sifts through data sets in search of patterns and relationshipspredictive analytics, which builds models to forecast customer behaviour and other future developmentsmachine learning, which taps algorithms to analyze large data setsdeep understanding, which is a more advanced offshoot of machine learningtext mining and statistical analysis softwareartificial intelligence (AI)mainstream business intelligence softwaredata visualization tools Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.Big data analytics uses and examplesHere are few examples of how big data analytics can be used to help organizations:Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.Big data analytics benefitsThe benefits of using big data analytics include:Quickly analyzing large amounts of data from different sources in many other formats and types.Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.Cost savings, which can result from new business process efficiencies and optimizations.A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.Improved, better-informed risk management strategies that draw from large sample sizes of data.
 Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details.  Here is a review of the four steps of the data development process:Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:internet clickstream data;webserver logs;mobile applications;cloud applications; social media content;text from customer and survey responses;mobile phone records; andmachine data obtained by sensors connected to the internet of things (IoT).Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:data mining, which sifts through data sets in search of patterns and relationshipspredictive analytics, which builds models to forecast customer behaviour and other future developmentsmachine learning, which taps algorithms to analyze large data setsdeep understanding, which is a more advanced offshoot of machine learningtext mining and statistical analysis softwareartificial intelligence (AI)mainstream business intelligence softwaredata visualization tools Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.Big data analytics uses and examplesHere are few examples of how big data analytics can be used to help organizations:Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.Big data analytics benefitsThe benefits of using big data analytics include:Quickly analyzing large amounts of data from different sources in many other formats and types.Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.Cost savings, which can result from new business process efficiencies and optimizations.A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.Improved, better-informed risk management strategies that draw from large sample sizes of data.


 

Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.

Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details. 

 

Here is a review of the four steps of the data development process:

Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:

internet clickstream data;

webserver logs;

mobile applications;

cloud applications; 

social media content;

text from customer and survey responses;

mobile phone records; and

machine data obtained by sensors connected to the internet of things (IoT).

Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.

Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.

The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:

data mining, which sifts through data sets in search of patterns and relationships

predictive analytics, which builds models to forecast customer behaviour and other future developments

machine learning, which taps algorithms to analyze large data sets

deep understanding, which is a more advanced offshoot of machine learning

text mining and statistical analysis software

artificial intelligence (AI)

mainstream business intelligence software

data visualization tools

 

Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.

Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.

Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.

Big data analytics uses and examples

Here are few examples of how big data analytics can be used to help organizations:

Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.

Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.

Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.

Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.

Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.

Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.

Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.

Big data analytics benefits

The benefits of using big data analytics include:

Quickly analyzing large amounts of data from different sources in many other formats and types.

Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.

Cost savings, which can result from new business process efficiencies and optimizations.

A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.

Improved, better-informed risk management strategies that draw from large sample sizes of data.

Key Details
Software trial:

14 Days

Starting Price:

$29/Month

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